# Grounding Characters and Places in Narrative Texts

arXiv: https://arxiv.org/abs/2305.17561 · HTML: https://mlsanity.com/paper/2305.17561/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Inferring Interpersonal Relations in Narrative Summaries](https://mlsanity.com/paper/1512.00112/) (2015)

## Built on by (most cited first, 9 of 9)

- [CHIRON: Rich Character Representations in Long-Form Narratives](https://mlsanity.com/paper/2406.10190/) (2024)
- [CPopQA: Ranking Cultural Concept Popularity by LLMs](https://mlsanity.com/paper/2311.07897/) (2023)
- [Evaluating LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3](https://mlsanity.com/paper/2406.11380/) (2024)
- [Says Who? Effective Zero-Shot Annotation of Focalization](https://mlsanity.com/paper/2409.11390/) (2024)
- [Computational Narrative Understanding for Expressive Text-to-Speech](https://mlsanity.com/paper/2509.04072/) (2025)
- [Computational Representations of Character Significance in Novels](https://mlsanity.com/paper/2601.15508/) (2026)
- [Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interaction](https://mlsanity.com/paper/2509.00381/) (2025)
- [CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories](https://mlsanity.com/paper/2606.22454/) (2026)
- [How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling](https://mlsanity.com/paper/2609.02482/) (2026)

## Abstract (arXiv)

Tracking characters and locations throughout a story can help improve the understanding of its plot structure. Prior research has analyzed characters and locations from text independently without grounding characters to their locations in narrative time. Here, we address this gap by proposing a new spatial relationship categorization task. The objective of the task is to assign a spatial relationship category for every character and location co-mention within a window of text, taking into consideration linguistic context, narrative tense, and temporal scope. To this end, we annotate spatial relationships in approximately 2500 book excerpts and train a model using contextual embeddings as features to predict these relationships. When applied to a set of books, this model allows us to test several hypotheses on mobility and domestic space, revealing that protagonists are more mobile than non-central characters and that women as characters tend to occupy more interior space than men. Overall, our work is the first step towards joint modeling and analysis of characters and places in narrative text.